1. Univariate modeling Sarah Medland

2. Starting at the beginning… Data preparation – The algebra style used in Mx expects 1 line per case/family – (Almost) limitless number of families and variables – Missing data Default missing code is now NA No missing covariates/definition variables! – Quick R - http://www.statmethods.net/

3. Selecting and sub-setting data Make separate data sets for the MZ and DZ Check data is numeric and behaves as expected

4. Common problem Problem: data contains a non numeric value Equivalent Mx Classic error - Uh-oh... I'm having trouble reading a number in D or E format

5. Important structural stuff openMx has a very fluid and flexible stucture Each code snippet is being saved as a variable We tend to reuse the variable names in our scripts This makes it very important to create a new project for each series of analyses Remember the project also contains the data so these files can become very large.

6. Matrices are the building blocks Many types eg. type="Lower" Denoted by names eg. name="a“ Size eg. nrow=nv, ncol=nv All estimated parameters must be placed in a matrix & Mx must be told what type of matrix it is mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=.6, label="a11", name="a" ), #X

7. 7 Choosing the model Thinking about parameter space… Imagine an ACE model Solution space bounded by CIs

8. Choosing the model ACE vs ADE – With twins alone can’t joint estimate ACDE – Options Add in an extra relationship Fix one of these parameters and estimate the other 3 Accept this limitation – All models are wrong some are useful (George E. P. Box) Reject the twin model, pretend genes have no influence and interpret biological inheritance as a social phenomenon – No 1 size fits all solution

9. Choosing the model

10. 10 Yesterday we ran an ADE Model Why? 0.300.78

11. 11 What is D again? Dominance refers to non-additive genetic effects resulting from interactions between alleles at the same locus or different loci (epistasis)

12. 12 What is D again? DZ twins/full siblings share – ~50% of their segregating DNA & – for ~25% loci they share not only the genotype but also the parental origin of each allele

13. 13 DZ twins/full siblings share – ~50% of their segregating DNA & – for ~25% loci they share not only the genotype but also the parental origin of each allele Consider a mating between mother AB x father CD: Sib 2 Sib1 ACADBCBD AC2110 AD1201 BC1021 BD0112 IBD 0 : 1 : 2 = 25% : 50% : 25% This is where the.5A comes from This is where the.25D comes from

14. Today we will run an ACE model Twin 1 E CA 1 1 1 Twin 2 A CE 1 1 1 1 1/.5 e a ca e c a 2 +c 2 +e 2.5a 2 +c 2 a 2 +c 2 +e 2 a 2 +c 2 a 2 +c 2 +e 2 MZDZ

15. Today we will run an ACE model Additive genetic effects Why is the coefficient for DZ pairs.5? Average genetic sharing between siblings/DZ twins a 2 +c 2 +e 2.5a 2 +c 2 a 2 +c 2 +e 2 a 2 +c 2 a 2 +c 2 +e 2 MZDZ Sib 2 Sib1 ACADBCBD AC2110 AD1201 BC1021 BD0112

16. Today we will run an ACE model Common environmental effects Coefficient =1 for MZ and DZ pairs Equal environment assumption – for all the environmental influences THAT MATTER there is ON AVERAGE no differences in the degree of environmental sharing between MZ and DZ pairs a 2 +c 2 +e 2.5a 2 +c 2 a 2 +c 2 +e 2 a 2 +c 2 a 2 +c 2 +e 2 MZDZ

17. Today we will run an ACE model Open RStudio faculty/sarah/tues_morning Copy everything

18. Today we will run an ACE model Twin 1 E CA 1 1 1 Twin 2 A CE 1 1 1 1 1/.5 e a ca e c

19. Today we will run an ACE model a 2 +c 2 +e 2.5a 2 +c 2 a 2 +c 2 +e 2 a 2 +c 2 a 2 +c 2 +e 2 MZDZ

20. To fit a model to data, the differences between the observed covariance matrix and model-implied expected covariance matrix are minimized. Objective functions are functions for which free parameter values are chosen such that the value of the objective function is minimized. mxFIMLObjective() uses full-information maximum likelihood to provide maximum likelihood estimates of free parameters in the algebra defined by the covariance and means arguments.

21. This models requires path parameters, means, covariance, data and objectives Automatic naming – you don’t need to predefine this

22. Submodels Pickup the previously prepared model Edit as required Rerun and compare

23. Saving your output Save the R workspace – On closing click yes – Very big – Saves everything Save the fitted model – Equivalent to save in classic Mx – save(univACEFit, file="test.omxs") – load("test.omxs") – need to load OpenMx first

24. What to report Summary statistics – Usually from a simplified ‘saturated’ model Standardized estimates – Easier to conceptualise ie 40% of the phenotypic variance vs a genetic effect of 2.84 Can easily be returned to original scale if summary statistics are provided

25. What to report Path coefficients – Very important in multivariate analyses Gives a much clearer picture of the directionality of effects Variance components/proportion of variance explained Genetic correlations

26. General Advice/Problem solving Scripting styles differ Check the sample description Learn to love the webpage Comments are your friends

27. Bus shelter on the road to Sintra (Portugal)